Merge pull request #14445 from l-bat:batchnorm3d

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -29,6 +29,8 @@ class BatchNormLayerImpl CV_FINAL : public BatchNormLayer
 public:
     Mat weights_, bias_;
     UMat umat_weight, umat_bias;
+    mutable int dims;
+
 
     BatchNormLayerImpl(const LayerParams& params)
     {
@@ -142,6 +144,7 @@ public:
                          std::vector<MatShape> &outputs,
                          std::vector<MatShape> &internals) const CV_OVERRIDE
     {
+        dims = inputs[0].size();
         if (!useGlobalStats && inputs[0][0] != 1)
             CV_Error(Error::StsNotImplemented, "Batch normalization in training mode with batch size > 1");
         Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
@@ -150,9 +153,9 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        return backendId == DNN_BACKEND_OPENCV ||
+        return (backendId == DNN_BACKEND_OPENCV) ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide()) ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && (preferableTarget == DNN_TARGET_CPU || dims == 4));
     }
 
 #ifdef HAVE_OPENCL
@@ -178,11 +181,12 @@ public:
         }
 
         UMat &inpBlob = inputs[0];
-        CV_Assert(inpBlob.dims == 2 || inpBlob.dims == 4);
         int groups = inpBlob.size[0];
         int channels = inpBlob.size[1];
-        int rows = inpBlob.dims > 2 ? inpBlob.size[2] : 1;
-        int cols = inpBlob.dims > 2 ? inpBlob.size[3] : 1;
+        int planeSize = 1;
+        for (size_t i = 2; i < inpBlob.dims; i++) {
+            planeSize *= inpBlob.size[i];
+        }
 
         String opts = (use_half) ? " -DDtype=half" : " -DDtype=float";
         for (size_t ii = 0; ii < outputs.size(); ii++)
@@ -196,7 +200,7 @@ public:
             }
             else
             {
-                MatShape s = shape(groups * channels, rows * cols);
+                MatShape s = shape(groups * channels, planeSize);
                 UMat src = inputs[ii].reshape(1, s.size(), &s[0]);
                 UMat dst = outputs[ii].reshape(1, s.size(), &s[0]);
                 int number = (s[1] % 8 == 0) ? 8 : ((s[1] % 4 == 0) ? 4 : 1);
@@ -248,9 +252,10 @@ public:
         CV_Assert(inputs.size() == 1);
 
         Mat &inpBlob = inputs[0];
-        CV_Assert(inpBlob.dims == 2 || inpBlob.dims == 4);
-        int rows = inpBlob.dims > 2 ? inpBlob.size[2] : 1;
-        int cols = inpBlob.dims > 2 ? inpBlob.size[3] : 1;
+        int planeSize = 1;
+        for (size_t i = 2; i < inpBlob.dims; i++) {
+            planeSize *= inpBlob.size[i];
+        }
 
         for (size_t ii = 0; ii < outputs.size(); ii++)
         {
@@ -262,8 +267,8 @@ public:
                 {
                     float w = weights_.at<float>(n);
                     float b = bias_.at<float>(n);
-                    Mat inpBlobPlane(rows, cols, CV_32F, inpBlob.ptr<float>(num, n));
-                    Mat outBlobPlane(rows, cols, CV_32F, outBlob.ptr<float>(num, n));
+                    Mat inpBlobPlane(1, planeSize, CV_32F, inpBlob.ptr<float>(num, n));
+                    Mat outBlobPlane(1, planeSize, CV_32F, outBlob.ptr<float>(num, n));
                     inpBlobPlane.convertTo(outBlobPlane, CV_32F, w, b);
                 }
             }

modules/dnn/test/test_onnx_importer.cpp

@@ -167,6 +167,13 @@ TEST_P(Test_ONNX_layers, BatchNormalization)
     testONNXModels("batch_norm");
 }
 
+TEST_P(Test_ONNX_layers, BatchNormalization3D)
+{
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
+        throw SkipTestException("");
+    testONNXModels("batch_norm_3d");
+}
+
 TEST_P(Test_ONNX_layers, Transpose)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE &&

modules/dnn/test/test_tf_importer.cpp

@@ -188,6 +188,13 @@ TEST_P(Test_TensorFlow_layers, batch_norm)
     runTensorFlowNet("mvn_batch_norm_1x1");
 }
 
+TEST_P(Test_TensorFlow_layers, batch_norm3D)
+{
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
+        throw SkipTestException("");
+    runTensorFlowNet("batch_norm3d");
+}
+
 TEST_P(Test_TensorFlow_layers, slim_batch_norm)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)